| Tensor decompositions for learning latent variable models A Anandkumar, R Ge, D Hsu, SM Kakade, M Telgarsky arXiv preprint arXiv:1210.7559 64, 70-72, 0 | 1104* | |
| Born again neural networks T Furlanello, Z Lipton, M Tschannen, L Itti, A Anandkumar International Conference on Machine Learning, 1607-1616, 2018 | 614 | 2018 |
| signSGD: Compressed optimisation for non-convex problems J Bernstein, YX Wang, K Azizzadenesheli, A Anandkumar International Conference on Machine Learning, 560-569, 2018 | 560* | 2018 |
| Stochastic activation pruning for robust adversarial defense GS Dhillon, K Azizzadenesheli, ZC Lipton, J Bernstein, J Kossaifi, ... arXiv preprint arXiv:1803.01442, 2018 | 422 | 2018 |
| Distributed algorithms for learning and cognitive medium access with logarithmic regret A Anandkumar, N Michael, AK Tang, A Swami IEEE Journal on Selected Areas in Communications 29 (4), 731-745, 2011 | 332 | 2011 |
| Deep active learning for named entity recognition Y Shen, H Yun, ZC Lipton, Y Kronrod, A Anandkumar arXiv preprint arXiv:1707.05928, 2017 | 330 | 2017 |
| A method of moments for mixture models and hidden Markov models A Anandkumar, D Hsu, SM Kakade Conference on Learning Theory, 33.1-33.34, 2012 | 328 | 2012 |
| Non-convex robust PCA P Netrapalli, N UN, S Sanghavi, A Anandkumar, P Jain Advances in Neural Information Processing Systems 27, 2014 | 300 | 2014 |
| A spectral algorithm for latent dirichlet allocation A Anandkumar, DP Foster, DJ Hsu, SM Kakade, YK Liu Advances in neural information processing systems 25, 2012 | 300 | 2012 |
| Learning latent tree graphical models MJ Choi, VYF Tan, A Anandkumar, AS Willsky Journal of Machine Learning Research 12, 1771-1812, 2011 | 265 | 2011 |
| Fourier neural operator for parametric partial differential equations Z Li, N Kovachki, K Azizzadenesheli, B Liu, K Bhattacharya, A Stuart, ... arXiv preprint arXiv:2010.08895, 2020 | 262 | 2020 |
| A tensor spectral approach to learning mixed membership community models A Anandkumar, R Ge, D Hsu, S Kakade Conference on Learning Theory, 867-881, 2013 | 259 | 2013 |
| Tensorly: Tensor learning in python J Kossaifi, Y Panagakis, A Anandkumar, M Pantic arXiv preprint arXiv:1610.09555, 2016 | 235 | 2016 |
| Beating the perils of non-convexity: Guaranteed training of neural networks using tensor methods M Janzamin, H Sedghi, A Anandkumar arXiv preprint arXiv:1506.08473, 2015 | 234 | 2015 |
| Opportunistic spectrum access with multiple users: Learning under competition A Anandkumar, N Michael, A Tang 2010 Proceedings IEEE INFOCOM, 1-9, 2010 | 188 | 2010 |
| SegFormer: Simple and efficient design for semantic segmentation with transformers E Xie, W Wang, Z Yu, A Anandkumar, JM Alvarez, P Luo Advances in Neural Information Processing Systems 34, 2021 | 181 | 2021 |
| Learning sparsely used overcomplete dictionaries via alternating minimization A Agarwal, A Anandkumar, P Jain, P Netrapalli SIAM Journal on Optimization 26 (4), 2775-2799, 2016 | 166 | 2016 |
| Neural lander: Stable drone landing control using learned dynamics G Shi, X Shi, M O’Connell, R Yu, K Azizzadenesheli, A Anandkumar, ... 2019 International Conference on Robotics and Automation (ICRA), 9784-9790, 2019 | 152 | 2019 |
| Efficient approaches for escaping higher order saddle points in non-convex optimization A Anandkumar, R Ge Conference on learning theory, 81-102, 2016 | 143 | 2016 |
| Efficient exploration through bayesian deep q-networks K Azizzadenesheli, E Brunskill, A Anandkumar 2018 Information Theory and Applications Workshop (ITA), 1-9, 2018 | 139 | 2018 |
